您将学到什么 (What you'll learn)

Learn how Intel has optimized many popular deep learning frameworks for modern Intel processors to deliver a significant improvement over default CPU backends in these frameworks and understand how to take full advantage of the performance of modern Intel processors

描述 (Description)

本讲话将用英语授课，同时会提供中文同声传译。中文版本摘要会在英文摘要下面给出。

Deep learning (DL) has seen phenomenal growth in the last few years and is being applied to solve problems in a variety of areas such as image classification, speech recognition, and object detection. Deep learning frameworks like TensorFlow, Caffe, MXNet, and PyTorch allow data scientists to implement neural network models to solve various problems. Intel engineers and framework owners have optimized these frameworks to improve their performance on Intel Xeon processor-based platforms. Huma Abidi details these collaborative optimization efforts and explains how deep learning framework users can leverage these optimizations. Along the way, Huma provides specific tuning tips to get the best performance on Intel Xeon processors.

Huma Abidi

Intel

Huma Abidi is the engineering director of the Artificial Intelligence Product Group at Intel, where she is responsible for deep learning framework software optimization for Intel Xeon processors. Huma joined Intel as software engineer and has since worked in a variety of engineering, validation, and management roles in the area of compilers, binary translation, and machine learning and deep learning. She received the Intel Achievement Award for her work in the Software and Services Group and was twice recognized with the Intel Software Quality award. She is passionate about women’s education and serves on the board of directors at ROSHNI, a philanthropic organization that educates and supports underprivileged girls in India. Huma holds a BS in pre-med and chemistry and an MS in computer science from the University of Massachusetts.